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This third edition provides chemical engineers with process control techniques that are used in practice while offering detailed mathematical analysis. Numerous examples and simulations are used to illustrate key theoretical concepts. New exercises are integrated throughout several chapters to reinforce concepts. Up-to-date information is also included on real-time optimization and model predictive control to highlight the significant impact these techniques have on industrial practice. And chemical engineers will find two new chapters on biosystems control to gain the latest perspective in the field.

Introduction To Process Control

Introduction to Process Control

Representative Process Control Problems

Illustrative Example

Classification of Process Control Strategies

A More Complicated Example--A Distillation Column

The Hierarchy of Process Control Activities

An Overview of Control System Design

Theoretical Models of Chemical Processes

The Rationale for Process Modeling

General Modeling Principles

Degrees of Freedom Analysis

Dynamic Models of Representative Processes

Solution of Dynamic Models and the Use of Digital Simulators

Dynamic Behavior Of Processes

Laplace Transforms

The Laplace Transform of Representative Functions

Solution of Differential Equations by Laplace Transform Techniques

Partial Fraction Expansion

Other Laplace Transform Properties

A Transient Response Example

Transfer Function and State-Space Models

Illustrative Example: A Continuous Blending System

Transfer Functions of Complicated Models

Properties of Transfer Functions

Linearization of Nonlinear Models

Dynamic Behavior of First-Order and Second-Order Processes

Standard Process Inputs

Response of First-Order Processes

Response of Integrating Processes

Response of Second-Order Processes

Dynamic Response Characteristics of More Complicated Processes

Poles and Zeros and Their Effect on Process Response

Processes with Time Delays

Approximation of Higher-Order Transfer Functions

Interacting and Noninteracting Processes

State-Space and Transfer Function Matrix Models

Multiple-Input, Multiple-Output (MIMO) Processes

Development of Empirical Dynamic Models from Process Data

Model Development Using Linear or Nonlinear Regression

Methods for Fitting First-Order and Second-Order Models Using Step Tests